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In the realm of ballistic target analysis, micro-motion attributes, such as warhead precession, nutation, and decoy oscillations, play a pivotal role. This paper addresses these critical aspects by introducing an advanced analytical model for assessing the Doppler power spectra of convex quadric revolution bodies during precession. Our model is instrumental in calculating the Doppler shifts pertinent to both precession and swing cones. Additionally, it extends to delineate the Doppler power spectra for configurations involving cones and sphere–cone combinations. A key aspect of our study is the exploration of the effects exerted by geometric parameters and observation angles on the Doppler spectra, offering a comparative perspective of various micro-motion forms. The simulations distinctly demonstrate how different micro-motion patterns of a cone influence the Doppler power spectra and underscore the significance of geometric parameters and observational angles in shaping these spectra. This research not only contributes to enhancing LIDAR target identification methodologies but also lays a groundwork for future explorations into complex micro-motions like nutation.

UAV transmission tower inspection is the use of UAV technology for regular inspection and troubleshooting of towers on transmission lines, which helps to improve the safety and reliability of transmission lines and ensures the stability of the power supply. From the traditional manual tower boarding to the current way of manually selecting target camera shooting points from 3D point clouds to plan the inspection path of the UAV, operational efficiency has drastically improved. However, indoor planning work is still labor-consuming and expensive. In this paper, a deep learning-based point cloud transmission tower segmentation (PCTTS) model combined with the corresponding target point localization algorithm is proposed for automatic segmentation of transmission tower point cloud data and automatically localizing the key inspection component as the target point for UAV inspection. First, we utilize octree sampling with unit ball normalization to simplify the data and ensure translation invariance before putting the data into the model. In the feature extraction stage, we encode the point set information and combine Euclidean distance and cosine similarity features to ensure rotational invariance. On this basis, we adopt multi-scale feature extraction, construct a local coordinate system, and introduce the offset-attention mechanism to enhance model performance further. Then, after the feature propagation module, gradual up-sampling is used to obtain the features of each point to complete the point cloud segmentation. Finally, combining the segmentation results with the target point localization algorithm completes the automatic extraction of UAV inspection target points. The method has been applied to six kinds of transmission tower point cloud data of part segmentation results and three kinds of transmission tower point cloud data of instance segmentation results. The experimental results show that the model achieves mIOU of 94.1% on the self-built part segmentation dataset and 86.9% on the self-built instance segmentation dataset, and the segmentation accuracy outperforms that of the methods for point cloud segmentation, such as PointNet++, DGCNN, Point Transformer, and PointMLP. Meanwhile, the experimental results of UAV inspection target point localization also verify the method’s effectiveness in this paper.

Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed. The raw images, which are captured by snapshot multi-spectral imaging systems, require a reconstruction procedure called demosaicing to estimate a fully defined multi-spectral image (MSI). With increasing spectral bands, the challenge of demosaicing becomes more difficult. Furthermore, the existing demosaicing methods will produce adverse artifacts and aliasing because of the adverse effects of spatial interpolation and the inadequacy of the number of layers in the network structure. In this paper, a novel multi-spectral demosaicing method based on a deep convolution neural network (CNN) is proposed for the reconstruction of full-resolution multi-spectral images from raw MSFA-based spectral mosaic images. The CNN is integrated with the channel attention mechanism to protect important channel features. We verify the merits of the proposed method using 5 × 5 raw mosaic images on synthetic as well as real-world data. The experimental results show that the proposed method outperforms the existing demosaicing methods in terms of spatial details and spectral fidelity.

Soft rock engineering is involved in many important engineering fields such as mining, hydraulic engineering, transportation and national defense. With the increase of mining depth and the development of tunnel engineering, a large number of tunnels and roadways need to pass through soft rock formations, in which the problems such as high geostress and broken and weak surrounding rocks are prominent. Large deformation disasters of soft rocks pose serious threats to engineering safety and cause enormeous economic losses. In this paper, the research progress on soft rock support in China is first reviewed, and the research status quo of technology for soft rock control for large deformation hazards is summarized in the following aspects: (1) passive support methods represented by improved rigid support, retractable support and compound lining; (2) reinforced active support technology using high-strength bolts and cables; (3) soft rock modification technology using grouting; (4) soft rock reinforcement with pressure relief as the core idea; and (5) compound support methods. Furthermore, the development of different supporting technologies and methods is elaborated, and the applicable conditions, advantages and disadvantages of different supporting methods are analyzed. It is usually difficult to meet the demand of large deformation control of soft rock relying on a single support method. Therefore, it is urgent to solve the problems of the prevention and control of large deformation disaster of soft rock to realize the efficient collaborative control among different supporting measures and achieve the real-time accurate monitoring of deformation and stress fields. Finally, based on the above research results, the development tendency of support technology for soft rock with large deformation hazards is prospected and the countermeasures are proposed.

The effects of electrolyzing water-cooled green tea with negative redox potential on the quality of tea soup was studied by comparing the bottled natural water-cooled green tea and conventional hot bubble green tea, in order to provide a new choice for the processing water of cold bubble green tea with high content of active ingredient and strong antioxidant activity. Electrolyzing drinking water by adjusting the electrolysis time to generate electrolytic water samples 1 and 2 with different negative redox potentials. The basic physicochemical indexes, main active ingredient, antioxidant activity, sensory quality and aroma components of electrolyzed water with negative redox potential, bottled natural water-cooled green tea and conventional hot bubble green tea were determined and compared by UV spectrophotometry, high performance liquid chromatography, headspace solid-phase microextraction and gas chromatography-mass spectrometry. The results showed that the pH of electrolytic water-cooled green tea soup was weak alkaline, the redox potential was negative, the content of soluble solids and the extraction rate of tea soup were significantly higher than those of bottled natural water-cooled green tea (P

In this study, we aimed to characterize the liver protein profile of Chaohu ducks using two-dimensional electrophoresis and proteomics. The livers were quickly collected from 120 healthy, 84-day-old Chaohu ducks. The intramuscular fat (IMF) content of the left pectoralis muscle was determined using the Soxhlet extraction method. The total protein of liver tissues from the high and low IMF groups was extracted for proteomics. Functional enrichment analysis of the differentially expressed proteins (DEPs) was conducted using gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG). In total, 43 DEPs were identified. Functional enrichment analysis indicated that these DEPs were significantly related to four lipid metabolic processes: carboxylic acid metabolic process, ATP metabolic process, oxoacid metabolic process, and organic acid metabolic process. Three pathways correlated with lipid metabolism were identified using KEGG analysis: glycolysis/gluconeogenesis, pentose phosphate pathway, fructose, and mannose metabolism. Eight key proteins associated with lipid metabolism were identified: ALDOB, GAPDH, ENO1, RGN, TPI1, HSPA9, PRDX1, and GPX1. Protein–protein interaction analysis revealed that the glycolysis/gluconeogenesis pathway mediated the interaction relationship. Key proteins and metabolic pathways were closely related to lipid metabolism and showed a strong interaction in Chaohu ducks.

IntroductionBrain atrophy is a critical biomarker of disease progression and treatment response in neurodegenerative diseases such as multiple sclerosis (MS). Confounding factors such as inconsistent imaging acquisitions hamper the accurate measurement of brain atrophy in the clinic. This study aims to develop and validate a robust deep learning model to overcome these challenges; and to evaluate its impact on the measurement of disease progression.MethodsVoxel-wise pseudo-atrophy labels were generated using SIENA, a widely adopted tool for the measurement of brain atrophy in MS. Deformation maps were produced for 195 pairs of longitudinal 3D T1 scans from patients with MS. A 3D U-Net, namely DeepBVC, was specifically developed overcome common variances in resolution, signal-to-noise ratio and contrast ratio between baseline and follow up scans. The performance of DeepBVC was compared against SIENA using McLaren test-retest dataset and 233 in-house MS subjects with MRI from multiple time points. Clinical evaluation included disability assessment with the Expanded Disability Status Scale (EDSS) and traditional imaging metrics such as lesion burden.ResultsFor 3 subjects in test-retest experiments, the median percent brain volume change (PBVC) for DeepBVC and SIENA was 0.105 vs. 0.198% (subject 1), 0.061 vs. 0.084% (subject 2), 0.104 vs. 0.408% (subject 3). For testing consistency across multiple time points in individual MS subjects, the mean (± standard deviation) PBVC difference of DeepBVC and SIENA were 0.028% (± 0.145%) and 0.031% (±0.154%), respectively. The linear correlation with baseline T2 lesion volume were r = −0.288 (p < 0.05) and r = −0.249 (p < 0.05) for DeepBVC and SIENA, respectively. There was no significant correlation of disability progression with PBVC as estimated by either method (p = 0.86, p = 0.84).DiscussionDeepBVC is a deep learning powered brain volume change estimation method for assessing brain atrophy used T1-weighted images. Compared to SIENA, DeepBVC demonstrates superior performance in reproducibility and in the context of common clinical scan variances such as imaging contrast, voxel resolution, random bias field, and signal-to-noise ratio. Enhanced measurement robustness, automation, and processing speed of DeepBVC indicate its potential for utilisation in both research and clinical environments for monitoring disease progression and, potentially, evaluating treatment effectiveness.

Increasing environmental pollution has led to the spread of many endocrine-disrupting chemicals (EDCs) around the world, which are toxic substances in the form of compounds that pose a great threat to the reproductive health of mammals and become a potential cause of many reproductive function-related diseases. In the past decade, the rapid development of single-cell RNA sequencing (scRNA-seq) technology has greatly promoted the study of the toxic mechanisms of EDCs in the mammalian reproductive system, including DEHP, ZEN, BPA, and BDE47. These studies aim to resolve the interference of EDCs in critical stages of reproductive development, including prepubertal and pubertal in males, meiosis I and early follicle formation in females. This paper introduces the sequencing process and analysis methods of current mainstream scRNA-seq technology, systematically reviews the outstanding contributions and specific research ideas of this technology in the study of reproductive system toxicity, lists representative cases of using this technology to explore reproductive damage caused by EDCs, and summarizes in detail the connection between environmental pollution and reproductive development disorders. It provides an important theoretical basis and direction for further exploring the mechanism of damage to the physiological functions of toxic substances on the reproductive system and the prevention and treatment of reproductive diseases.

In order to provide a reference for improving the physicochemical properties of starch, the study of starch polyphenol complex interaction has aroused considerable interest. As a common method of starch modification, ultrasound can make starch granules have voids and cracks, and make starch and polyphenols combine more closely. In this research, canistel seed starch was modified by ultrasonic treatment alone or combined with quercetin. The molecular structure, particle characteristics and properties of starch were evaluated. With the increase of ultrasonic temperature, the particle size of the dextrinized starch granules increased, but the addition of quercetin could protect the destruction of starch granule size by ultrasonic; X-ray diffraction and infrared spectra indicated that quercetin was bound to the surface of canistel seed starch through hydrogen bonding, and the complex and the original starch had the same crystal structure and increased crystallinity; by molecular simulation, quercetin bound inside the starch molecular helix preserved the crystalline helical configuration of starch to some extent and inhibited the complete unhelicalization of starch molecules. Meanwhile, hydrogen bonding was the main driving force for the binding of starch molecules to quercetin, and van der Waals interactions also promoted the binding of both. In the physicochemical properties, as the temperature increased after the combination of ultrasonic modified starch combined with quercetin, the solubility, swelling force and apparent viscosity of the compound increased significantly, and it has higher stability and shear resistance.

A nanoarray structure was constructed on the quartz glass cover surface, which had both strong dust removal ability and significant anti-reflection. Firstly, a structure model of dust removal nanoarray was established to investigate the effects of the structural parameters such as the period, the depth, and the duty ratio. Then a nanoarray structure was prepared on the glass surface with nano-imprinting and plasma etching, then modified with atomic layer deposition to adjust its structural parameters. The results show that the dust removal performance of the glass cover improved by about 14% after being constructed a 500-nm period nanoarray structure. As the etching time and the number of deposited layers increased, the dust removal performance first increased and then decreased. After 30 min etching and 50 layers of atomic deposition, the nanoarray improved the dust removal performance of the glass cover by about 45%. Moreover, this nanoarray structure also had an obvious anti-reflection function, improving the transmittance within 350–1100 nm of the glass cover by 0.63% before dust fall, by 1.23% after the dust fall at a 30° inclination angle, and by 1.51% at a 60° inclination angle.

Background and introductionFederated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's applications remain suboptimal in neuroimage analysis tasks such as lesion segmentation in multiple sclerosis (MS), due to variance in lesion characteristics imparted by different scanners and acquisition parameters.MethodsIn this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance. In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.ResultsThe proposed method has been validated on two FL MS segmentation scenarios using public and clinical datasets. Specifically, the case-wise and voxel-wise Dice score of the proposed method under the first public dataset is 65.20 and 74.30, respectively. On the second in-house dataset, the case-wise and voxel-wise Dice score is 53.66, and 62.31, respectively.Discussions and conclusionsThe Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by significantly outperforming other FL methods. Furthermore, the segmentation performance of FL incorporating our proposed aggregation mechanism can achieve comparable performance to that from centralized training with all the raw data.

Lithium–sulfur batteries (LSBs) are one of the main candidates for the next generation of energy storage systems. To improve the performance of LSBs, we herein propose the use of strained MoS2 (s-MoS2) as a catalytically active sulfur host. The introduction of strain in the MoS2 surface, which alters its atomic positions and expands the S–Mo–S angle, shifts the d-band center closer to the Fermi level and provides the surface with abundant and highly active catalytic sites; these enhance the catalyst's ability to adsorb lithium polysulfides (LiPS), accelerating its catalytic conversion and promoting lithium-ion transferability. Strain is generated through the synthesis of core–shell nanoparticles, using different metal sulfides as strain-inducing cores. s-MoS2 nanoparticles are supported on carbon nanofibers (CNF/s-MoS2), and the resulting electrodes are characterized by capacities of 1290 and 657 ​mAh g−1 ​at 0.2 and 5 ​C, respectively, with a 0.05% capacity decay rate per cycle at 8 ​C during 700 cycles. Overall, this work not only provides an ingenious and effective strategy to regulate LiPS adsorption and conversion through strain engineering, but also indicates a path toward the application of strain engineering in other energy storage and conversion fields.

A small subpopulation of colorectal cancer stem cells (CSCs) possess the ability to self-renew and the capacity to initiate the original tumor. EpCAMhigh/CD44+ cells are regarded as CSCs in colorectal cancer. The present study was undertaken to investigate the significance of EpCAM in the in vitro proliferation ability and oxaliplatin chemoresistance of EpCAMhigh/CD44+ colorectal CSCs.

Abstract The Yinggehai Basin (YGH) in the South China Sea is a Cenozoic, high‐temperature, and high‐pressure basin. Exploration and development of natural gas resources in the YGH show that owing to the diapir structures make the formation pressure system is complex, overpressure characteristics are obvious, prediction is difficult, and spatial distribution is irregular, which severely restricts the development of natural gas. To evaluate the influence of diapir structures on the formation and distribution of overpressure, we compared and analyzed the ancient and modern geothermal temperatures, organic matter maturity, clay mineral evolution, burial depth, and pressure coefficient in the diapir and nondiapir zones of the YGH. The results showed that of the diapir structures in the YGH occurred in the following order: Uplift–spiking–piercing–depositing. The Dongfang area is dominated by low‐amplitude turtle‐back diapir structures with weak energy, whereas the Ledong area is dominated by high‐amplitude spiked diapir structures with strong energy, both of which pressurize the upper formation. The Changnan area is dominated by high‐amplitude pierced/depositing diapirs with strong energy, which can relieve pressure. The influence of the diapir and its associated structures on the formation of overpressure can be attributed to the transport of the diapir body and the associated fault‐fracture system and the local thermal effect caused by the upwelling of hot fluids. Under the influence of the diapir structure, the overpressure top surface of the basin was shallower. According to the degree of influence and intensity of the diapir structure, variations in formation pressure coefficient with depth can be divided into four evolution modes: constant pressure, slow pressurization, “Z” pressurization, and “S” pressurization types. This study provides some guidance and references for the identification and accurate prediction of the overpressure formation mechanism in basins containing diapirs, and helps in formulating efficient drilling and production plans.

Abstract Sand deposition in the wellbore is one of the most common problems in oil–gas and hydrate exploitation work. A detailed understanding of the migration characteristics of the sand bed and accurate prediction of the starting velocity of the sand bed are key aspects of effective sand carrying. Therefore, this study conducted a large‐scale outdoor gas–water–sand three‐phase flow experiment. The effects of critical factors such as flow pattern, sand size, sand bed height, and pipe inclination angle on sand bed startup velocity and migration were studied experimentally. The experimental results indicated that during the startup process of the sand bed, sand particles exhibited three states: rolling, jumping, and suspension. During the experiment, the sand bed morphology included a static bed, rolling bed, jumping bed, and suspension bed. Compared to a stratified flow, intermittent flow and agitated flow were more beneficial for the startup velocity of sand beds. An increase in pipe inclination also reduced the starting velocity of the sand bed. With an increase in sand size, the startup velocity of the sand bed increased correspondingly. However, there was no significant influence on the migration morphology of the sand bed. Additionally, the greater the thickness of the sand bed, the lower the starting velocity of the sand bed. Based on the stress states of sand grains, in combination with experimental data, this study used least‐squares estimation to establish a prediction model for the startup velocity of sand beds. The model prediction results were in good agreement with the experimental data. This study will provide a basis for predicting the startup velocity of sand beds during the gas–liquid two‐phase sand‐carrying process.

Abstract Formation pressure is the most fundamental data in oil and gas drilling and production; it has an important position in the entire cycle of oil and gas extraction. However, most current prediction methods are limited to parametric methods with fixed models; such that the accuracy does not meet requirements. This is especially true for deeper layers of marine sedimentary basins where the safety density window is extremely narrow. In this study, we propose a novel method to predict pore pressure using machine learning techniques. For the first time, the effective stress (direct output variable) was accurately predicted by a combination of four input variables (2900 sets of data, of which 90% is the training subset and 10% is the testing subset), including longitudinal velocity, porosity, mud content, and density. As such, an accurate prediction of the formation pressure was achieved based on the effective stress theorem. The performance of machine learning techniques was verified by comparing and analyzing the prediction results with traditional parametric single and multivariate models; whereby the best algorithm was chosen by structural optimization and comparative analysis of five algorithms (multilayer perceptron neural network, radial basis neural network, support vector machine, random forest, and gradient boosting machine). Compared with the methods based on parametric one‐dimensional and multivariate models, the machine learning‐based method was determined to possess high accuracy, adequate self‐adaptation, and high fault tolerance (D2 = 0.9981, RMSE = 0.00718 g/cm3). Moreover, the multilayer perceptual neural network algorithm outperformed other machine learning algorithms in terms of goodness of fit, generalization, and prediction accuracy, with D2 = 0.9981 and RMSE = 0.00709 g/cm3. The formation pressure prediction model developed in this study is not affected by the mechanical depositional environment and is applicable to sandy mudstone formations, such that it can be a useful and highly accurate alternative to the traditional formation pressure prediction methods with fixed parameter forms.

In this work, we design and implement a user-friendly, AIempowered, auto-pipelining data analysis system for biological macromolecule crystallography. It consists of four modules, (1) data reduction that generates reference reflection files from X-ray diffraction images, (2) structure prediction via database-querying or AlphaFold/OpenFold real-time prediction, (3) molecular replacement and (4) module building and refinement. This data analysis system, currently at Work-In-Progress stage, is based on and developed for High Energy Photon Source initially, aiming at automatic, intelligent, and highefficiency software and will be open-source for academic research.

Plant-endophytic microbes affect plant growth, development, nutrition, and resistance to pathogens. However, how endophytic microbial communities change in different strawberry plant compartments after Fusarium pathogen infection has remained elusive. In this study, 16S and internal transcribed spacer rRNA amplicon sequencing were used to systematically investigate changes in the bacterial and fungal diversity and composition in the endophytic compartments (roots, stems, and leaves) of healthy strawberries and strawberries with Fusarium wilt, respectively. The analysis of the diversity, structure, and composition of the bacterial and fungal communities revealed a strong effect of pathogen invasion on the endophytic communities. The bacterial and fungal community diversity was lower in the Fusarium-infected endophytic compartments than in the healthy samples. The relative abundance of certain bacterial and fungal genera also changed after Fusarium wilt infection. The relative abundance of the beneficial bacterial genera Bacillus, Bradyrhizobium, Methylophilus, Sphingobium, Lactobacillus, and Streptomyces, as well as fungal genera Acremonium, Penicillium, Talaromyces, and Trichoderma, were higher in the healthy samples than in the Fusarium wilt samples. The relative abundance of Fusarium in the infected samples was significantly higher than that in the healthy samples, consistent with the field observations and culture isolation results for strawberry wilt. Our findings provide a theoretical basis for the isolation, identification, and control of strawberry wilt disease.

Objective To establish scrap indicators for key equipment in blood screening laboratory of blood centers and quantitatively assess the running status of key equipment, so as to provide a scientific method for equipment scrap. Methods Through the literature review and Delphi method, the scrap indicators of key equipment in blood screening laboratories were established in terms of applicability, economy and advancement of the equipment. The weights of relevant indicators were calculated by analysis hierarchy process (AHP), and the quantitative assessment model of equipment scrap priority was established according to the indicators and its weight. The equipment running data from January 2020 to December 2020 of Laboratory Department were collected and analyzed using the model, and its accuracy was verified based on experience. Results Thirteen second-level scrap indicators were established, and the weights of the three first-level indexes of applicability, economy and advancement were 0.582, 0.114 and 0.306, respectively. Among the total 30 key equipment, the model score of 4 equipment was less than 0.5, and the running status after manually checking met the scrap standard. Conclusion The model can accurately assess the scrap priority of key equipment and facilitate the procurement budget and scrapping identification in advance, which can avoid the waste of resources and ensure safe, efficient and orderly laboratory work.

Background: Copper is an indispensable mineral element involved in many physiological metabolic processes. Cuproptosis is associated with a variety of cancer such as hepatocellular carcinoma (HCC). The objective of this study was to examine the relationships between the expression of cuproptosis-related genes (CRGs) and tumor characteristics, including prognosis and microenvironment of HCC.Methods: The differentially expressed genes (DEGs) between high and low CRGs expression groups in HCC samples were identified, and further were analyzed for functional enrichment analysis. Then, CRGs signature of HCC was constructed and analyzed utilizing LASSO and univariate and multivariate Cox regression analysis. Prognostic values of CRGs signature were evaluated by Kaplan-Meier analysis, independent prognostic analysis and nomograph. The expression of prognostic CRGs was verified by Real-time quantitative PCR (RT-qPCR) in HCC cell lines. In addition, the relationships between prognostic CRGs expression and the immune infiltration, tumor microenvironment, antitumor drugs response and m6A modifications were further explored using a series of algorithms in HCC. Finally, ceRNA regulatory network based on prognostic CRGs was constructed.Results: The DEGs between high and low CRG expression groups in HCC were mainly enriched in focal adhesion and extracellular matrix organization. Besides, we constructed a prognostic model that consists of CDKN2A, DLAT, DLST, GLS, and PDHA1 CRGs for predicting the survival likelihood of HCC patients. And the elevated expression of these five prognostic CRGs was substantially in HCC cell lines and associated with poor prognosis. Moreover, immune score and m6A gene expression were higher in the high CRG expression group of HCC patients. Furthermore, prognostic CRGs have higher mutation rates in HCC, and are significantly correlated with immune cell infiltration, tumor mutational burden, microsatellite instability, and anti-tumor drug sensitivity. Then, eight lncRNA-miRNA-mRNA regulatory axes that affected the progression of HCC were predicted.Conclusion: This study demonstrated that the CRGs signature could effectively evaluate prognosis, tumor immune microenvironment, immunotherapy response and predict lncRNA-miRNA-mRNA regulatory axes in HCC. These findings extend our knowledge of cuproptosis in HCC and may inform novel therapeutic strategies for HCC.

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system, the most common type of dementia in old age, which causes progressive loss of cognitive functions such as thoughts, memory, reasoning, behavioral abilities and social skills, affecting the daily life of patients. The dentate gyrus of the hippocampus is a key area for learning and memory functions, and an important site of adult hippocampal neurogenesis (AHN) in normal mammals. AHN mainly consists of the proliferation, differentiation, survival and maturation of newborn neurons and occurs throughout adulthood, but the level of AHN decreases with age. In AD, the AHN will be affected to different degrees at different times, and its exact molecular mechanisms are increasingly elucidated. In this review, we summarize the changes of AHN in AD and its alteration mechanism, which will help lay the foundation for further research on the pathogenesis and diagnostic and therapeutic approaches of AD.

To solve the weak chaos in the spatiotemporal chaotic system based on coupled map lattices under some control parameters and the un-uniformity of sequences generated by the coupled map lattices, a two-dimensional pseudo-random coupled map lattices (2D-PRCML) system was proposed.Firstly, the two-dimensional partitioned elementary cellular automata (2D-PECA) was designed to establish pseudo-random coupling.Secondly, iterative results of 2D-PECA were utilized to perturb the 2D-PRCML system.The chaotic behaviors of the proposed system, such as the bifurcation diagram, Kolmogorov-Sinai entropy, were investigated.Moreover, the uniformity of sequences generated by the 2D-PRCML system was discussed, and the correlation coefficients between any two sequences generated by different lattices were acquired.The analyses and tests indicate that the 2D-PRCML system exhibits stronger chaotic behavior.Furthermore, the sequence generated by the proposed system possesses better uniformity, randomness, and unpredictability.The outstanding properties of the 2D-PRCML system prove that it is more suitable for applying in cryptography and chaotic secure communication.

A novel high-speed transceiver based on 7 bit correlated non-return-to-zero(CNRZ-7) with high-bandwidth-density(bandwidth per unit length) and low-power for Die-to-Die(D2D) communication is proposed. In order to further improve the SNR and the bandwidth of the CNRZ-5 in D2D communication, a CNRZ-7 based transmitter matrix and receiver matrix are proposed firstly, which are derived from Walsh Hadamard(W-H) transform and inverse transformation. In addition, to reduce the power consumption of the transmitter, the encoding driver based on CNRZ-7 transmitter matrix is designed with source series terminated drivers(SST). To further improve the SNR of the receiver, the decoding circuit based on CNRZ-7 receiver matrix is designed with a special multi-input comparators(MIC), which contain equalizer circuits. This transceiver is designed with a 28nm CMOS technology and the core area is 0.66mm2. The post-simulation results show that this transceiver can operate at 280 Gb/s, and the data rate is 35 Gb/s/wire. The worst width of the receiver’s eye-diagrams is 0.45UI when the transceiver operates at a 50 mm PCB channel with a 10dB@20GHz insertion loss, and the total BER is less than 1E-15. The power consumption is 1.1pJ/b under a normal corner.

Real-world problems always change over time. In the past two decades, many researchers have become increasingly interested in studying evolutionary optimization algorithms for dynamic problems owing to their great importance in real-world applications. Currently, there are no unified and mature test functions for dynamic problems, and the existing dynamic test functions are incapable of simulating a real dynamic environment. This study constructs a new benchmark test function generator for simulating dynamic environments that combine existing static benchmark functions and their related properties to form new dynamic test functions. It is simple to construct and conveniently control the dynamic properties by adjusting the corresponding parameters. Through a series of comparisons with the dynamic benchmark of the generalized dynamic benchmark generator (GDBG), the simulation results show that the dynamic test functions constructed by this generator can reflect different types of changing intensity.

Abstract Developing an efficient and quality remote sensing (RS) technology using volume and efficient modelling in different aircraft RS images is challenging. Generative models serve as a natural and convenient simulation method. Because aircraft types belong to the fine class under the rough class, the issue of feature entanglement may occur while modelling multiple aircraft classes. Our solution to this issue was a novel first-generation realistic aircraft type simulation system (ATSS-1) based on the RS images. It realised fine modelling of the seven aircraft types based on a real scene by establishing an adaptive weighted conditional attention generative adversarial network and joint geospatial embedding (GE) network. An adaptive weighted conditional batch normalisation attention block solved the subclass entanglement by reassigning the intra-class-wise characteristic responses. Subsequently, an asymmetric residual self-attention module was developed by establishing a remote region asymmetric relationship for mining the finer potential spatial representation. The mapping relationship between the input RS scene and the potential space of the generated samples was explored through the GE network construction that used the selected prior distribution z, as an intermediate representation. A public RS dataset (OPT-Aircraft_V1.0) and two public datasets (MNIST and Fashion-MNIST) were used for simulation model testing. The results demonstrated the effectiveness of ATSS-1, promoting further development of realistic automatic RS simulation.

Abstract Background During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect. Results Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt. Conclusions These results all indicate that the static model in pathways could simplify the detection of the early-warning signals.

This paper summarized the effect and related nursing management of Chinese herbal medicine cream external application combined with auricular acupoint pressing therapy in pain relief for a patient with hyperplasia of mammary glands. According to the anatomical structure of the breast and the physiological state of the breast under the influence of hormone levels, Chinese herbal cream external application combined with auricular acupoint pressing technique were carried out. Integrated nursing measures were carried out to relieve pain symptoms and anxiety status of the patient during the treatment. (本文总结中药膏摩联合耳穴贴压缓解1例乳腺增生症疼痛患者的护理经验。根据乳房的解剖结构, 乳腺生理状态受激素水平影响下呈周期性变化的规律, 采用乳腺中药膏摩联合耳穴贴压技术, 结合中西医护理措施, 明显改善患者乳房疼痛症状, 减轻患者焦虑。)

Lithium dendrite growth due to uneven electrodeposition may penetrate the separator and solid electrolyte, causing inner short circuit and potential thermal runaway. Despite great electrochemical phase-field simulation efforts devoted to exploring the dendrite growth mechanism under the temperature field, no unified picture has emerged. For example, it remains open how to understand the promotion, inhibition, and dual effects of increased temperature on dendrite growth when using different electrolyte types. Here, by comprehensively considering the temperature-dependent Li+ diffusion coefficient, electrochemical reaction coefficient, and initial temperature distribution in phase-field model, we propose that the activation–energy ratio, defined as the ratio of electrochemical reaction activation energy to electrolyte Li+ diffusion activation energy, can be used to quantify the effect of temperature on dendrite morphology. Specifically, we establish a mechanism diagram correlating the activation–energy ratio, uniform initial temperature, and maximum dendrite height, which unifies the seemingly contradictory simulation results. Furthermore, results based on nonuniform initial temperature distribution indicate that a positive temperature gradient along the discharging current facilitates uniform Li+ deposition and local hotspot should be avoided. These findings provide valuable insights into the temperature-dependent Li dendrite growth and contribute to the practical application of Li metal batteries.

Hypoxic-ischemic white matter injury (WMI) pathogenesis in preterm infants is not well established, and iron-related proteins in the brain may play an important role in imbalanced iron metabolism. We aimed to investigate the iron-related protein changes in neonatal rats after hypoxia-ischemia (HI), clarify the role of iron-related proteins in hypoxic-ischemic WMI, and potentially provide a new target for the clinical treatment of hypoxic-ischemic WMI in preterm infants. We adopted a WMI animal model of bilateral common carotid artery electrocoagulation combined with hypoxia in neonatal 3-day-old Sprague-Dawley rats. We observed basic myelin protein (MBP) and iron-related protein expression in the brain (ferritin, transferrin receptor [TfR], and membrane iron transporter 1 [FPN1]) via Western blot and double immunofluorescence staining. The expression of MBP in the WMI group was significantly downregulated on postoperative days (PODs) 14, 28, and 56. Ferritin levels were significantly increased on PODs 3, 7, 14, and 28 and were most significant on POD 28, returning to the sham group level on POD 56. FPN1 levels were significantly increased on PODs 7, 28, and 56 and were still higher than those in the sham group on POD 56. TfR expression was significantly upregulated on PODs 1, 7, and 28 and returned to the sham group level on POD 56. Immunofluorescence staining showed that ferritin, TfR, and FPN1 were expressed in neurons, blood vessels, and oligodendrocytes in the cortex and corpus callosum on POD 28. Compared with the sham group, the immune-positive markers of three proteins in the WMI group were significantly increased. The expression of iron-related proteins in the brain (ferritin, FPN1, and TfR) showed spatiotemporal dynamic changes and may play an important role in hypoxic-ischemic WMI.

Background: The unprecedented COVID-19 pandemic has been spreading for a long time. However, it is unclear whether the pandemic influenced admission in a surgical pediatric intensive care unit (PICU) that never received COVID-19 patients during the early outbreak in China. Methods: A retrospective study was conducted in a surgical PICU in a tertiary hospital in Chengdu, China. We sought to describe the trend in admission numbers from January 2018 to April 2021. We explored the impact of the COVID-19 outbreak on PICU admission characteristics by including all patients younger than 18 years admitted to the PICU between January 23 and April 8 in 2020 and those admitted in the same time periods in prepandemic years (2018 and 2019) and in 2021. Results: The percentage of patients admitted to the PICU from the Chengdu region increased from 34.2 percent in 2019 to 40.4 percent in 2020, whereas that from other provinces decreased from 11.7 percent in 2019 to 5.8 percent in 2020 (P = 0.012). The median length of stay (LOS) in the PICU was significantly longer in the 2020 cohort (4.0 days) than in the 2019 cohort (2.0 days) (P < 0.001); the median hospital LOS was also significantly longer in the former (12.0 days) than in the latter (8.0 days) (P < 0.001). Hospital outcomes (P = 0.005) and primary diagnosis distributions (P = 0.025) between the 2020 and 2019 cohorts were both statistically significant. Conclusions: In a surgical PICU that never received COVID-19 patients, the onset of the 2020 major outbreak was accompanied by changes in the composition of the regional sources of patients, longer PICU and hospital stays, increased proportions of unauthorized discharges, and changes in the distribution of primary diseases for admission. These findings have yet to be strengthened by additional studies involving similar healthcare backgrounds.

Summary: Ethylene carbonate (EC) in the electrolyte is not stable in cells operated at high voltage (≥4.4V) or with Li metal anode, which greatly reduce the energy density and lifetime of the rechargeable lithium battery. Herein, an EC-free linear alkyl carbonate-based electrolyte is developed, which enables the high-voltage (≥4.4V) and low-temperature (−30°C) application of Ni-rich cathode (LiNi0.8Mn0.1Co0.1O2, NCM811). The EC-free system, consisting of LiPF6 and LiNO3 in ternary linear alkyl carbonates, possesses low viscosity, weakly solvated structure, and high interfacial stability with both the Ni-rich cathode and the Li metal anode to avoid continuous electrode/electrolyte side reactions and metal dissolution from the cathode. As a result, the Li||NCM811 cell delivers remarkable capacity retention of 93 ± 0.5% at the voltage of 4.4V and 88 ± 0.6% at 4.5V over 100 cycles. This study provides very encouraging perspective to develop EC-free carbonate-based electrolyte for high-voltage and low-temperature application in high-energy-density rechargeable lithium batteries.

Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the “curse of dimensionality”, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes ℓ2,1-norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors.

With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Consequently, how to better utilize multi-source data to represent ground information has become an important research question in the field of geoscience. In this paper, a novel model called multi-modal transformer cascaded fusion net (MMTCFN) is proposed for fusion and classification of multi-modal remote sensing data, Hyperspectral Imagery (HSI) and LiDAR data. Feature fusion and feature extraction are the two stages of the model. First, in the feature extraction stage, a three-branch cascaded Convolutional Neural Network (CNN) framework is employed to fully leverage the advantages of convolutional operators in extracting shallow-level local features. Based on this, we generated multi-modal long-range integrated deep features utilizing the transformer-based vectorized pixel group transformer (VPGT) module during the feature fusion stage. In the VPGT block, we designed a vectorized pixel group embedding that preserves the global features extracted from the three branches in a non-overlapping multi-space manner. Moreover, we introduce the DropKey mechanism into the multi-head self-attention (MHSA) to alleviate overfitting caused by insufficient training samples. Finally, we employ a probabilistic decision fusion strategy to integrate multiple class estimations, assigning a specific category to each pixel. This model was experimented on three HSI-LiDAR datasets with balanced and unbalanced training samples. The proposed model outperforms the other seven SOTA approaches in terms of OA performance, proving the superiority of MMTCFN for the HSI-LiDAR classification task.

The contents of soap and organic acid in emulsion polymerized styrene-butadiene rubber determined by indicator titration and potentiometric titration in ISO 7781 were compared. The differences between the determination principles and results of the two methods were compared, and the accuracy of the results was investigated by standard addition experiment. The results showed that the results of potentiometric titration and indicator method were consistent. When testing the soap content in emulsion polymerized styrene-butadiene rubber, the indicator method was greatly affected by amine antioxidant and filling oil. But the potentiometric titration method was less affected by amine antioxidant and was not affected by filling oil, so the determination result was more accurate.

Background and objectives: Multivisceral resection (MVR) is advocated by some high-volume sarcoma centers. The objective of this study was to investigate the therapeutic effect of MVR in primary retroperitoneal sarcoma (RPS) with tumor burden less than 10 ​cm. Methods: A retrospective analysis of patients with primary RPS who underwent radical surgical resection from 2009 to 2021 with lesions smaller than the size of a baseball (10 ​cm) was carried out. The incidence of postoperative morbidity and postoperative local recurrence-free survival rate of the MVR group and the non-MVR group were compared by propensity score-matching analysis. Results: A total of 319 patients with primary RPS underwent surgical resection, of which 95 patients (29.8%) had a tumor burden less than 10 ​cm, with 24 patients (25.3%) involved in the MVR group and 71 patients (74.7%) in the non-MVR group. After matching, the two groups showed no statistical difference at baseline, while the MVR group reported more postoperative complications (P ​= ​0.008) and a longer median postoperative hospital stay (P ​= ​0.002). In terms of local recurrence-free survival, there were no statistical difference between the two groups (P ​= ​0.269). Conclusions: Primary RPS patients with tumors smaller than baseball may not benefit from aggressive surgical strategy.

Warburg effect is a pivotal hallmark of cancers and appears prevalently in renal cell carcinoma (RCC). FBP1 plays a negative role in Warburg effect as a rate-limiting enzyme in gluconeogenesis, yet its mechanism in RCC remains to be further characterized. Herein, we revealed that FBP1 was downregulated in RCC tissue samples and was related to the poor survival rate of RCC. Strikingly, miR-24-1 whose DNA locus is overlapped with enhancer region chr9:95084940-95087024 was closely linked with the depletion of FBP1 in RCC. Of note, miRNAs like miR-24-1 whose DNA loci are enriched with H3K27ac and H3K4me1 modifications are belonging to nuclear activating miRNAs (NamiRNAs), which surprisingly upregulate target genes in RCC through enhancer beyond the conventional role of repressing target gene expression. Moreover, miR-24-1 reactivated the expression of FBP1 to suppress Warburg effect in RCC cells, and subsequently inhibited proliferation and metastasis of RCC cells. In mechanism, the activating role of miR-24-1 was dependent on enhancer integrity by dual luciferase reporter assay and CRISPR/Cas9 system. Ultimately, animal assay in vivo validated the suppressive function of FBP1 on 786-O and ACHN cells. Collectively, the current study highlighted that activation of FBP1 by enhancer-overlapped miR-24-1 is capable of contributing to Warburg effect repression through which RCC progression is robustly blocked, providing an alternative mechanism for RCC development and as well implying a potential clue for RCC treatment strategy.

IntroductionThe detailed association between albumin levels and mortality has not been studied in critically ill children. The aim of this study was to reveal an association between albumin levels in detail and mortality in critically ill children.Materials and methodsWe retrospectively collected data from children admitted to four pediatric intensive care units (PICUs) in China between January 2015 and October 2020. Restricted cubic spline curves based on logistic regression models were generated to evaluate the detailed associations between serum albumin levels and PICU mortality. Threshold effect analysis was performed using two piecewise regression models.ResultsThe study included 9,123 children. The overall mortality was 5.3%. The detailed association between serum albumin levels and the risk of mortality followed a U-shape. The risk of mortality decreased with increasing serum albumin levels (OR = 0.919; 95% CI: 0.886, 0.954) in children with serum albumin levels < 43.2 g/L and increased with increasing serum albumin levels (OR = 1.174; 95% CI: 1.044, 1.316) in children with serum albumin levels ≥ 43.2 g/L.ConclusionThere was a U-shaped association between serum albumin levels and mortality in critically ill children in the PICU.

EMU (electric multiple unit) traction motors are powered by converters whose output voltage increases the voltage stress borne by the insulation system, making the ITSC (inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power is proposed in the article to evaluate the non-metallic ITSC faults extent. The apFFT (all-phase FFT) time-shift phase difference correction with double Hanning windows is used to calculate fault features to train the SVM (support vector machine) fault diagnosis model whose hyper-parameters C and g are optimized using grid search methods. The experimental verification was carried out on the EMU electric traction simulation experimental platform. According to the fault extent index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training dataset and 93.33% on the test dataset. There was no misclassification between normal and serious ITSC fault samples.

The North Konawe region in Indonesia, known for its lateritic nickel (Ni) deposits, holds significant potential for obtaining Ni resources. However, the complex topographic conditions of this area pose challenges. Exploring the application of remote sensing technology to reveal the spectral response mechanism of Ni grade from high-precision multispectral data and inversion of Ni grade represents a novel direction in future Ni resource exploration. Traditional remote sensing inversion methods solely consider the spectral characteristics of sample data and ignore vital geospatial location information. As a result, efficiently obtaining regional details of target substance content over large areas has become challenging. The introduction of the geographically weighted regression (GWR) method offers an opportunity for fine-grained Ni grade inversion based on remote sensing. This study focused on the E and K blocks within the lateritic Ni mining area in North Konawe. Through utilizing the WordView-3 multispectral data which exhibits immense potential in quantitative remote sensing inversion studies, GWR was employed to integrate spectral features and spatial information. The goal was to reveal the correlation between multispectral remote sensing data and Ni grade. The obtained results were then compared and analyzed with multiple linear regression (MLR) and back propagation neural network (BPNN) models. The findings revealed that GWR achieved the highest coefficient of determination R2 of 0.96, surpassing MLR and BPNN values of 0.05 and 0.17, respectively. Additionally, GWR exhibited the lowest root mean square error of 0.04, which was lower than those of MLR and BPNN with the values of 0.25 and 0.23, respectively. These results confirmed the enhanced stability and accuracy of the GWR method compared to MLR and BPNN. Furthermore, GWR effectively mapped the spatial distribution trends of Ni grades in the study area, providing evidence of the method’s effectiveness in Ni grade inversion. The study also delved into the inversion effect of the GWR method in areas with varying weathering crust thickness and vegetation cover. The research revealed that higher values of weathering crust thickness negatively impacted the inversion effect. However, the influence mechanism of vegetation cover on Ni grade inversion necessitated further investigation. These results served as a significant demonstration of the remote sensing inversion of mineral resource grades in similar areas. They provided valuable insights for future exploration and decision-making processes.

Micro-LED display technology is considered to be the next generation of display technology, which has the characteristics of high miniaturization, thin film and integration, as well as the advantages of high brightness, high contrast, fast response speed and long service life. However, in the development of Micro-LED display technology, there are still some technical and cost problems to be solved. This paper focuses on the key technologies involved in Micro-LED display technology, such as chip technology, mass transfer, full-color display, bonding and driving technology, the research history and frontier progress of these technologies are reviewed in detail. For chip epitaxy technology, the wavelength uniformity, current density and defect control are emphasized; for chip process, the two main chip structures and the challenges brought by miniaturization are discussed emphatically; for chip integration, full-color display, backplane bonding and driving, several mainstream technical schemes are summarized in turn. Finally, the chip detection and repair technologies and commercial application are introduced.

The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%.

This paper proposes colaGAE, a self-supervised learning framework for graph-structured data. While graph autoencoders (GAEs) commonly use graph reconstruction as a pretext task, this simple approach often yields poor model performance. To address this issue, colaGAE employs mutual isomorphism as a pretext task for a continuous latent space sampling GAE (colaGAE). The central idea of mutual isomorphism is to sample from multiple views in the latent space and reconstruct the graph structure, with significant improvements in terms of the model’s training difficulty. To investigate whether continuous latent space sampling can enhance GAEs’ learning of graph representations, we provide both theoretical and empirical evidence for the benefits of this pretext task. Theoretically, we prove that mutual isomorphism can offer improvements with respect to the difficulty of model training, leading to better performance. Empirically, we conduct extensive experiments on eight benchmark datasets and achieve four state-of-the-art (SOTA) results; the average accuracy rate experiences a notable enhancement of 0.3%, demonstrating the superiority of colaGAE in node classification tasks.

The basic requirement of the 5th Generation (5G) network is to realize automatic end-to-end service arrangement and slice orchestration. In this paper, considering the purpose of the open standardization of the Northbound Interface (NBI) of 5G transport network, we first analyze the functional requirements of the NBI. Then the modeling scheme of the NBI is proposed. Finally, the different technical implementation schemes of 5G transport network are shielded, which provides a reference for the unification of the NBI.

Background: Renal cell carcinoma (RCC) is the predominant type of malignant tumor in kidney cancer. Finding effective biomarkers, particularly those based on the tumor immune microenvironments (TIME), is critical for the prognosis and diagnosis of RCC. Increasing evidence has revealed that long non-coding RNAs (lncRNAs) play a crucial role in cancer immunity. However, the comprehensive landscape of immune infiltration-associated lncRNAs and their potential roles in the prognosis and diagnosis of RCC remain largely unexplored.Methods: Based on transcriptomic data of 261 RCC samples, novel lncRNAs were identified using a custom pipeline. RCC patients were classified into different immune groups using unsupervised clustering algorithms. Immune-related lncRNAs were obtained according to the immune status of RCC. Competing endogenous RNAs (ceRNA) regulation network was constructed to reveal their functions. Expression patterns and several tools such as miRanda, RNAhybrid, miRWalk were used to define lncRNAs-miRNAs-mRNAs interactions. Univariate Cox, LASSO, and multivariate Cox regression analyses were performed on the training set to construct a tumorigenesis-immune-infiltration-related (TIR)-lncRNA signature for predicting the prognosis of RCC. Independent datasets involving 531 RCC samples were used to validate the TIR-lncRNA signature.Results: Tens of thousands of novel lncRNAs were identified in RCC samples. Comparing tumors with controls, 1,400 tumorigenesis-related (TR)-lncRNAs, 1269 TR-mRNAs, and 192 TR-miRNAs were obtained. Based on the infiltration of immune cells, RCC patients were classified into three immune clusters. By comparing immune-high with immune-low groups, 241 TIR-lncRNAs were identified, many of which were detected in urinary samples. Based on lncRNA-miRNA-mRNA interactions, we constructed a ceRNA network, which included 25 TR-miRNAs, 28 TIR-lncRNAs, and 66 TIR-mRNAs. Three TIR lncRNAs were identified as a prognostic signature for RCC. RCC patients in the high-risk group exhibited worse OS than those in the low-risk group in the training and testing sets (p < 0.01). The AUC was 0.9 in the training set. Univariate and multivariate Cox analyses confirmed that the TIR-lncRNA signature was an independent prognostic factor in the training and testing sets.Conclusion: Based on the constructed immune-related lncRNA landscape, 241 TIR-lncRNAs were functionally characterized, three of which were identified as a novel TIR-lncRNA signature for predicting the prognosis of RCC.

Bauhinia variegata petals are colorful, rich in anthocyanins, and have ornamental, nutritional, and medicinal value. However, the regulatory mechanism of anthocyanin accumulation in B. variegata remains unclear. In this study, a combined analysis of the metabolome and transcriptome was performed in red and white B. variegata cultivars in the early, middle, and blooming stages. A total of 46 different anthocyanins were identified, of which 27 showed marked differences in accumulation between the two cultivars, and contribute to their different petal colors. Malvidin 3-O-galactoside, peonidin 3-O-galactoside, cyanidin 3-O-glucoside, cyanidin 3-O-galactoside, and malvidin 3-O-glucoside were much more abundant in the second stage of flowering. In the blooming stage, except for the anthocyanins mentioned, delphinidin 3-O-galactoside and petunidin 3-O-galactoside were the most abundant anthocyanins in the red flowers, indicating that malvidin, peonidin, cyanidin, delphinidin, and petunidin were all responsible for the red color of petals in B. variegata. RNA sequencing identified 2,431 differentially expressed genes (DEGs), of which 26 were involved in the anthocyanin synthesis pathway. Correlations between the anthocyanin biosynthesis-related DEGs and anthocyanin contents were explored, and the DEGs involved in anthocyanin accumulation in B. variegata petals were identified. Eighteen of these DEGs encoded key catalytic enzymes, such as anthocyanidin reductase (ANR) and flavonoid-3′5′-hydroxylase (F3′5′H), and 17 of them encoded transcription factors (TFs) belonging to 14 families (including MYB, NAC, SPL, ERF, and CHR28). These results improve our understanding of the roles of anthocyanins, catalytic enzymes, and TFs in B. variegata petal-color expression.

BackgroundAlthough erectile dysfunction (ED) often occurs simultaneously with depression, not all patients with ED suffer major depression (MD), with a PHQ-9 score ≥15 indicating MD. Because the PHQ-9 questionnaire includes phrases such as “I think I am a loser” and “I want to commit suicide,” the psychological burdens of ED patients are likely to increase inevitably after using the PHQ-9, which, in turn, may affect ED therapeutic effects. Accordingly, we endeavored to develop a nomogram to predict individual risk of PHQ-9 score ≥15 in these patients.MethodsThe data of 1,142 patients with ED diagnosed in Xijing Hospital and Northwest Women and Children's Hospital from January 2017 to May 2020 were analyzed. While the Least Absolute Shrinkage and Selection Operator regression was employed to screen PHQ-9 score ≥15 related risk factors, multivariate logistic regression analysis was performed to verify these factors and construct the nomogram. The training cohort and an independent cohort that comprised 877 prospectively enrolled patients were used to demonstrate the efficacy of the nomogram.ResultsThe IIEF-5 score, PEDT score, physical pain score, frequent urination, and feeling of endless urination were found to be independent factors of PHQ-9 score ≥15 in patients with ED. The nomogram developed by these five factors showed good calibration and discrimination in internal and external validation, with a predictive accuracy of 0.757 and 0.722, respectively. The sensitivity and specificity of the nomogram in the training cohort were 0.86 and 0.52, respectively. Besides, the sensitivity and specificity of the nomogram in the validation cohort were 0.73 and 0.62, respectively. Moreover, based on the nomogram, the sample was divided into low-risk and high-risk groups.ConclusionThis study established a nomogram to predict individual risk of PHQ-9 score ≥15 in patients with ED. It is deemed that the nomogram may be employed initially to avoid those with a low risk of MD completing questionnaires unnecessarily.

This study aimed to establish nomograms to preoperatively predict the possibility of testicular salvage (TS) in patients with testicular torsion. The clinical data of 204 patients with testicular torsion diagnosed at Xijing Hospital and Tangdu Hospital (Xi'an, China) between August 2008 and November 2019 were retrospectively analyzed. Univariate and multivariate logistic regression analyses were used to determine the independent predictors of TS. Based on multivariate regression coefficients, nomograms to predict possibility of TS were established. The predictive ability of the nomograms was internally validated by receiver operating characteristic (ROC) curves and calibration plots. The duration of symptoms ranged from 2 h to 1 month, with a median of 3.5 days. Thirty (14.7%) patients underwent surgical reduction and contralateral orchiopexy, while the remaining 174 (85.3%) underwent orchiectomy and contralateral orchiopexy. Finally, long symptom duration was an independent risk predictor for TS, while visible intratesticular blood flow and homogeneous testicular echotexture under color Doppler ultrasound were independent protective predictors. Internal validation showed that the nomograms, which were established by integrating these three predictive factors, had good discrimination ability in predicting the possibility of TS (areas under the ROC curves were 0.851 and 0.828, respectively). The calibration plots showed good agreement between the nomogram-predicted possibility of TS and the actual situation. In conclusion, this brief preoperative prediction tool will help clinicians to quickly determine the urgency of surgical exploration.

The coupling of communication networks plays an indispensable role in modern power systems; however, it also leads to many problems due to cascading failure. Although most current research focuses on the single-layer, non-interacting system and physical layer network, few studies have combined the complex network theory to ignore the influence of communication networks. To address this problem, this paper introduces a cascading failure model of a cyber physical power system (CPPS) that considers the operation characteristics of the communication layer, especially the influence of transmission delay and connectivity on stability control. First, the influence mode of the communication layer failure node acting on the physical layer is analyzed, and the interaction mechanism between the communication layer and physical layer is clarified. Furthermore, the propagation process of failure in the CPPS system is modeled, and the influence of information transmission delay on the stability control process is considered. In this manner, the development process of cascading failure in a CPPS system is described accurately. Finally, according to the loss rate of the physical layer load, the influence of the communication layer node is evaluated to verify the accuracy of the model. This paper also provides a practical model of a CPPS system, which is helpful for building a secure and reliable energy Internet.

The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can finally fly a safer path with high accuracy in an unknown environment with static or dynamic obstacles, and avoid local minima or solve local oscillation at the same time.